Metadata, literally referred to as “data about data,” has been widely used in organizing information. The creation and management of metadata has primarily been the responsibility of information professionals engaged in cataloguing, classification, and indexing. As information has become increasingly computerized and digitalized, metadata has involved in the management and interoperability of data management systems and administrative functions. For example, metadata of a file specifies a collection of attributes that describe the file such as the size in bytes, the path, the last modified time, the owner, the accessibility and so on.
Nowadays many network services such as a cloud storage service require a database system to manage and maintain metadata when various operations are performed on a file. The operations with intensive low spatial and temporal locality requests such as reading, writing, deleting, or relocating files on a cloud system limit the disk scheduling flexibility and results in inefficient use of a buffer memory since most database systems are not optimized in randomness of operations. As the number of metadata increases, the input/output (I/O) speed accordingly decreases and the overall performance is significantly limited thereby. Conventional metadata accessing methods are seriously challenged when facing input workloads that are update-intensive with low access locality. Therefore, to boost the performance of workloads on a cloud storage system or other network services dominated by low locality is to optimizedly reduce disk I/O operations.